Mind, Body, World- Foundations of Cognitive Science, 2013a

and not in the composite nor in a machine that the Perception is to be sought.
(Leibniz, 1902, p. 254)
Leibniz called these simple substances monads and argued that all complex experiences
were combinations of monads. Leibniz’ monads are clearly an antecedent of
the architectural primitives that we have been discussing over the last few pages.
Just as thoughts are composites in the sense that they can be built from their component
monads, an algorithm is a combination or sequence of primitive processing
steps. Just as monads cannot be further decomposed, the components of an architecture
are not explained by being further decomposition, but are instead explained
by directly appealing to physical causes. Just as the Leibniz mill’s monads would
look like working pieces, and not like the product they created, the architecture
produces, but does not resemble, complete algorithms.
The Chinese room would be a more compelling argument against the possibility
of machine intelligence if one were to look inside it and actually see its knowledge.
This would mean that its homunculi were not discharged, and that intelligence was
not the product of basic computational processes that could be implemented as
physical devices.
2.12 Levelling the Field
The logic machines that arose late in the nineteenth century, and the twentieth-century
general-purpose computers that they evolved into, are examples of information
processing devices. It has been argued in this chapter that in order to explain
such devices, four different vocabularies must be employed, each of which is used
to answer a different kind of question. At the computational level, we ask what
information processing problem is being solved by the device. At the algorithmic
level, we ask what procedure or program is being used to solve this problem. At the
architectural level, we ask from what primitive information capabilities is the algorithm
composed. At the implementational level, we ask what physical properties are
responsible for instantiating the components of the architecture.
As we progress from the computational question through questions about
algorithm, architecture, and implementation we are moving in a direction that
takes us from the very abstract to the more concrete. From this perspective each of
these questions defines a different level of analysis, where the notion of level is to
be taken as “level of abstractness.” The main theme of this chapter, then, is that to
fully explain an information processing device one must explain it at four different
levels of analysis.
The theme that I’ve developed in this chapter is an elaboration of an approach
with a long history in cognitive science that has been championed in particular
by Pylyshyn (1984) and Marr (1982). This historical approach, called the tri-level
Multiple Levels of Investigation 51